• Korea journal of population studies
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• v.11 no.1
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• pp.58-75
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• 1988

1.Objectives of the Study It is a well known fact that a prompt and reliable data on demographic information is essential in a proper planning and evaluation of any program of national or community level. Especially vital statistics are an important demographic component among demographic information. Realizing the importance of vital statistics, the government has made some efforts for years to improve the vital registration system which has a close relationship with the production of vital statistics. However, it is still observed that there are some limitations in utilizing vital registration data due to considerable amount of vital events which are never registered and registered but not in time or inaccurately, even though vital registration system in Korea has sound legal basis. In this connection, the objectives of the study is as follows :(1) To examine some problems of the vital registration system in various aspects, (2) To make improvement programme of continuous Demographic Survey as a supplementary source of vital statistics, and (3) To find out some alternatives for making it possible to produce and utilize the reliable vital statistics by developing analytical methodologies on that. 2. Current Situation of Vital Registration System All the vital events, i.e. births, deaths, marriages and divorces, are to be registered in time under the Civil Registration Law, Statistics Law and Regulation on Vital Statstics as a duty of people. Some recent tendencies in each of recent registration are summarized as below: (1) The completeness of vital registration .Out of all births which are occurred during a year, around 75% of those compared to the estimates are registered in the year of occurrence. .In case of death registration, the percentage of registration in the year of occurrene has been gradually increased from 86.2% in the year of 1980, but it is still below the level of 90% compared to the estimates. .The percentage of registration for marriages and divorces in the year of occurrence out of total registered numbers was revealed to be 69% and 73% respectively in 1985. (2) Continuous Demographic Survey .It is a kind of sample survey for the purpose of producing reliable vital statistics which could not be provided by the vital registration. .It covers about 17, 000 sample households at national level and important information for vital events are collected in every month by 323 expertized enumerators who are regular staff of the government. .Although the result of the survey seems to be more reliable than of vital registration, the reliability of the data is still bellow the acceptable level if compared with relevant information from other sources such as population census or special surveys. 3. Problems of Vital Registration System There are four major obstacles in improving vital registration system in Korea; (1) In general, policy priority is not given on any programme of improving vital registration system. It is, therefore, very difficult to formulate comprehensive programme through having cooperation from related authorities and sufficient financial assistance. (2) In all the laws related and system itself, there is substantial degree of overlap and irrationality. Registration of each vital event is maintained according to several laws and regulation such as Civil Registration Law, Statistics Law, Resident Registration Law and Regulation on Vital Statistics. However they are mutually overlapped and overall supervision can not be done systematically due to lack of co-operation among the authorities concerned. (3) The administration of vital registration system seems to be working inefficiently, because of most of civil servants who are in charge of vital registration are lacking of conception on vital statistics and also there is a certain extent of regidity in handling the works. Therefore, they are doing their jobs in a passive way. (4) A substantial proportion of vital events occurred is not registered within the legal time limit (i.e. within one month after the occurrence in case of birth and death) or not registered forever. Some of social customs and superstitution seem to be the potential causes especially in case of births and deaths. 4. Recommendations for the Improvement of Vital Statistics (1) Reporting systems such as civil registration, vital statistics and resident registration should be integrated under the single law. Also, administrative supervision, personnel and budget with regard to the registration system should be under the control of a single ministry. (2) It is necessary to simplify the procedures and methods of reporting vital events, i.e., reducing number of sheets of the form, making corrections easily, reducing registration items, etc. (3) Continuous Demographic Survey as a supplementary source of vital registration should be improved and special ad-hoc surveys should be conducted wth regular interval. (4) In-depth analysis should be done using various sources of data on vital statistics. 5. Concluding Remarks From this study, we can notice that temporary campaign and motivation programs are not sufficient to improve the quality of vital statistics. Strong intentions and continuous efforts of the government are needed for the improvement of the vital registration system. Furthermore, most of the data collected through the registration are not properly analyzed and utilized, partly due to the lack of appreciation among high-level governmental officials of the need for vital statistics. It is, therefore, requested that long-term improvement programs of vital statistics be implemented with policy priority and continuous efforts be given to this purpose as a long-term goal of development in Korea.

• The Korean Journal of Air & Space Law and Policy
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• v.33 no.2
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• pp.315-343
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• 2018

In the event of an air accident, the media and members of the general public pay attention to the victim of the accident and are deeply concerned about their actions and rewards. However, through the accident of Air China(CCA) Flight 129, which occurred in 2002, we were able to confirm that it is a real problem that the victims of the air accident as well as the victims suffer much suffering and serious aftermath. Nevertheless, Korea's system for assistance the families of victims of air accident is very poor. On the other hand, when Trans-World Airlines(TWA) Flight 800 exploded and crashed over the Atlantic Ocean in 1996, the United States enacted a law to assistance the families of the victims of the accident. According to this law, systematic assistance and management of not only the victims of the accident but also their families, minimize the additional damage of victims and victims' families and help them to get rid of the accident after the accident. In particular, the measures taken by the US authorities in response to an accident in which an Asiana Airlines flight(AAR) 214 crashed during a landing at San Francisco International Airport in 2013, made a lot of suggestions for us to assistance the victims and their families in an air accident. The purpose of this paper is to suggest the necessity of improving the system for victims and victim's family assistance in air accident. In this paper, we analyze the domestic and foreign legal systems and related cases in past accidents, identify the deficiencies of the Korean system, and derive the necessity to improve the related system. It is also important to make sure that victims' families are relieved from early psychological and economic shocks and that the results of accident investigations are reliable. Relevant ministries, airlines, and related agencies should recognize that prompt and systematic assistance and cooperation is needed to ensure that victims and families are relieved of the impact and confidence in the investigation, as is the case in the United States. In addition, efforts should be made to supplement the related laws for the assistance of aircraft victims and victims' families, to establish manuals for implementation, to plan and to implement them promptly in the event of an accident. To achieve this, it is necessary to establish regulations for the legal institutionalization of the roles and responsibilities of national and state agencies on victims of aviation accidents and family assistance. And the victim and family assistance plan that the airline has to submit to it, as specified in the current law, need to specify that item. In addition, new and supplemented contents should be integrated into a single clause or proposed as a separate special law for the purpose of applying a clear law.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.261-268
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• 2021

Cloud computing is a computing paradigm in which users can utilize computing resources in a pay-as-you-go manner. In a cloud system, resources can be dynamically scaled up and down to the user's on-demand so that the total cost of ownership can be reduced. The Modeling and Simulation (M&S) technology is a renowned simulation-based method to obtain engineering analysis and results through CAE software without actual experimental action. In general, M&S technology is utilized in Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), Multibody dynamics (MBD), and optimization fields. The work procedure through M&S is divided into pre-processing, analysis, and post-processing steps. The pre/post-processing are GPU-intensive job that consists of 3D modeling jobs via CAE software, whereas analysis is CPU or GPU intensive. Because a general-purpose desktop needs plenty of time to analyze complicated 3D models, CAE software requires a high-end CPU and GPU-based workstation that can work fluently. In other words, for executing M&S, it is absolutely required to utilize high-performance computing resources. To mitigate the cost issue from equipping such tremendous computing resources, we propose HEMOS-Cloud service, an integrated cloud and cluster computing environment. The HEMOS-Cloud service provides CAE software and computing resources to users who want to experience M&S in business sectors or academics. In this paper, the economic ripple effect of HEMOS-Cloud service was analyzed by using industry-related analysis. The estimated results of using the experts-guided coefficients are the production inducement effect of KRW 7.4 billion, the value-added effect of KRW 4.1 billion, and the employment-inducing effect of 50 persons per KRW 1 billion.

• Korean Journal of Ecology and Environment
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• v.54 no.1
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• pp.49-60
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• 2021

We examined the flow characteristics by direct runoff and base flow in a headwater stream during observed 59 rainfall events of flood season (June~September) from 2017 to 2020 yrs. Total precipitation ranged from 5.0 to 400.8 mm, total runoff ranged from 0.1 to 176.5 mm, and runoff ratio ranged from 0.1 to 242.9% during the rainfall events. From hydrograph separation, flow duration in base flow (139.3 days) was tended to be longer than direct runoff (78.3 days), while the contribution of direct runoff in total runoff (54.2%) was greater than base flow (45.8%). The total amount and peak flow of direct runoff and base flow had the highest correlation (p<0.05) with total precipitation and duration of rain among rainfall and soil moisture conditions. Dominant rainfall events for the total amount and peak flow of base flow were generated under 5.0~200.4 and 10.5~110.5 mm in total precipitation. However, when direct runoff occurred as dominant rainfall events, total amount and peak flow were increased by 267.4~400.8 and 169.0~400.8 mm in total precipitation. Therefore, the unique aspects of our study design permitted us to draw inferences about flow characteristic analysis with the contribution of base flow and/or direct runoff in the total runoff in a headwater stream. Furthermore, it will be useful for the long-term strategy of effective water management for integrated surface-groundwater in the forested headwater stream.

• Journal of Bio-Environment Control
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• v.31 no.1
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• pp.67-76
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• 2022

This study aimed to determine the photosynthesis and growth characteristics of Peucedanum japonicum T. grown under aquaponics in a plant factory (AP) by comparing those grown under hydroponic cultivation system (HP). The AP system raised 30 fishes at a density of 10.6 kg·m^-3 in a 367.5 L tank, and at HP, nutrient solution was controlled with EC 1.3 dS·m^-1 and pH 6.5. The pH level ranged from 4.0 to 7.1 for the AP system and 4.0 to 7.4 for the HP system. The pH level in the AP began to decrease with an increase in nitrate nitrogen (NO₃-N) and lasted bellower than pH 5.5 for 15-67 DAT. It was found that ammonium nitrogen (NH₄-N) continued to increase even under low pH conditions. EC was maintained at 1.3 to 1.5 dS·m^-1 in both systems. The concentration of major mineral elements in the fish tank was higher than that of the hydroponics, except for K and Mg. There was no significant difference in the photosynthesis characteristics, but the PIABS parameters were 30.4% lower in the AP compared to the HP at the 34DAT and 12.0% lower at the 74DAT. There was no significant difference in the growth characteristics, but the petiole length was 56% longer in the leaf grown under the AP system. While there was no significant difference in the fresh and dry weights of leaf and root, the leaf area ratio was 36.43% higher in the AP system. All the integrated results suggest that aquaponics is a highly-sustainable farming to safely produce food by recycling agricultural by-products, and to produce Peucedanum japonicum as much as hydroponics under a proper fish density and pH level.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.121-142
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• 2023

As the number and weight of imported food are steadily increasing, safety management of imported food to prevent food safety accidents is becoming more important. The Ministry of Food and Drug Safety conducts on-site inspections of foreign food facilities before customs clearance as well as import inspection at the customs clearance stage. However, a data-based safety management plan for imported food is needed due to time, cost, and limited resources. In this study, we tried to increase the efficiency of the on-site inspection by preparing a machine learning prediction model that pre-selects the companies that are expected to fail before the on-site inspection. Basic information of 303,272 foreign food facilities and processing businesses collected in the Integrated Food Safety Information Network and 1,689 cases of on-site inspection information data collected from 2019 to April 2022 were collected. After preprocessing the data of foreign food facilities, only the data subject to on-site inspection were extracted using the foreign food facility_code. As a result, it consisted of a total of 1,689 data and 103 variables. For 103 variables, variables that were '0' were removed based on the Theil-U index, and after reducing by applying Multiple Correspondence Analysis, 49 characteristic variables were finally derived. We build eight different models and perform hyperparameter tuning through 5-fold cross validation. Then, the performance of the generated models are evaluated. The research purpose of selecting companies subject to on-site inspection is to maximize the recall, which is the probability of judging nonconforming companies as nonconforming. As a result of applying various algorithms of machine learning, the Random Forest model with the highest Recall_macro, AUROC, Average PR, F1-score, and Balanced Accuracy was evaluated as the best model. Finally, we apply Kernal SHAP (SHapley Additive exPlanations) to present the selection reason for nonconforming facilities of individual instances, and discuss applicability to the on-site inspection facility selection system. Based on the results of this study, it is expected that it will contribute to the efficient operation of limited resources such as manpower and budget by establishing an imported food management system through a data-based scientific risk management model.

• Journal of the Korean Institute of Landscape Architecture
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• v.52 no.1
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• pp.46-58
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• 2024

Pollinators are organisms that carry out the pollination process of plants and include Hymenoptera, Lepidoptera, Diptera, and Coleoptera. Among them, bees not only pollinate plants but also improve urban green spaces damaged by land use changes, providing a habitat and food for birds and insects. Today, however, the number of pollinating plants is decreasing due to issues such as early flowering due to climate change, fragmentation of green spaces due to urbanization, and pesticide use, which in turn leads to a decline in bee populations. The decline of bee populations directly translates into problems, such as reduced biodiversity in cities and decreased food production. Urban beekeeping has been proposed as a strategy to address the decline of bee populations. However, there is a problem asurban beekeeping strategies are proposed without considering the complex structure of the socio-ecological system consisting of bees foraging and pollination activities and are therefore unsustainable. Therefore, this study aims to analyze the socio-ecological system of honeybees, which are pollinators, structurally using system thinking and propose a green space planning strategy to revitalize urban beekeeping. For this study, previous studies that centered on the social and ecological system of bees in cities were collected and reviewed to establish the system area and derive the main variables for creating a causal loop diagram. Second, the ecological structure of bees' foraging and pollination activities and the structure of bees' ecological system in the city were analyzed, as was the social-ecological system structure of urban beekeeping by creating an individual causal loop diagram. Finally, the socio-ecological system structure of honey bees was analyzed from a holistic perspective through the creation of an integrated causal loop diagram. Citizen participation programs, local government investment, and the creation of urban parks and green spaces in idle spaces were suggestedas green space planning strategies to revitalize urban beekeeping. The results of this study differ from previous studies in that the ecological structure of bees and the social structure of urban beekeeping were analyzed from a holistic perspective using systems thinking to propose strategies, policy recommendations, and implications for introducing sustainable urban beekeeping.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.183-203
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• 2018

News articles are the most suitable medium for examining the events occurring at home and abroad. Especially, as the development of information and communication technology has brought various kinds of online news media, the news about the events occurring in society has increased greatly. So automatically summarizing key events from massive amounts of news data will help users to look at many of the events at a glance. In addition, if we build and provide an event network based on the relevance of events, it will be able to greatly help the reader in understanding the current events. In this study, we propose a method for extracting event networks from large news text data. To this end, we first collected Korean political and social articles from March 2016 to March 2017, and integrated the synonyms by leaving only meaningful words through preprocessing using NPMI and Word2Vec. Latent Dirichlet allocation (LDA) topic modeling was used to calculate the subject distribution by date and to find the peak of the subject distribution and to detect the event. A total of 32 topics were extracted from the topic modeling, and the point of occurrence of the event was deduced by looking at the point at which each subject distribution surged. As a result, a total of 85 events were detected, but the final 16 events were filtered and presented using the Gaussian smoothing technique. We also calculated the relevance score between events detected to construct the event network. Using the cosine coefficient between the co-occurred events, we calculated the relevance between the events and connected the events to construct the event network. Finally, we set up the event network by setting each event to each vertex and the relevance score between events to the vertices connecting the vertices. The event network constructed in our methods helped us to sort out major events in the political and social fields in Korea that occurred in the last one year in chronological order and at the same time identify which events are related to certain events. Our approach differs from existing event detection methods in that LDA topic modeling makes it possible to easily analyze large amounts of data and to identify the relevance of events that were difficult to detect in existing event detection. We applied various text mining techniques and Word2vec technique in the text preprocessing to improve the accuracy of the extraction of proper nouns and synthetic nouns, which have been difficult in analyzing existing Korean texts, can be found. In this study, the detection and network configuration techniques of the event have the following advantages in practical application. First, LDA topic modeling, which is unsupervised learning, can easily analyze subject and topic words and distribution from huge amount of data. Also, by using the date information of the collected news articles, it is possible to express the distribution by topic in a time series. Second, we can find out the connection of events in the form of present and summarized form by calculating relevance score and constructing event network by using simultaneous occurrence of topics that are difficult to grasp in existing event detection. It can be seen from the fact that the inter-event relevance-based event network proposed in this study was actually constructed in order of occurrence time. It is also possible to identify what happened as a starting point for a series of events through the event network. The limitation of this study is that the characteristics of LDA topic modeling have different results according to the initial parameters and the number of subjects, and the subject and event name of the analysis result should be given by the subjective judgment of the researcher. Also, since each topic is assumed to be exclusive and independent, it does not take into account the relevance between themes. Subsequent studies need to calculate the relevance between events that are not covered in this study or those that belong to the same subject.